Inputs

The stacked bar chart gives global insights on the Educational attainment by race in the San Francisco county:

• The global education attainment among the San Francisco county is very high: more than half the population has a Bachelor’s degree or higher.
• The White Alone population is much overrepresented among the Bachelor’s degree or higher category and underrepresented in the lower categories, especially Less than high school diploma and High school graduate (includes equivalency).
• The Asian alone population seems to be unequally distributed among the different categories, with an overrepresentation among the Less than high school diploma education attainment.
• The Two or More Races population seems to be mostly represented in the Bachelor’s degree or higher category, and Native Hawaiian and Other Pacific Islander Alone people equally distributed among the High school graduate (includes equivalency), Some college or associate’s degree and Bachelor’s degree or higher categories.

To get more details on the actual repartition of races among the educational attainment categories, you will want to have a look at the filled bar chart giving information on the proportion of each race in every category instead of the total numbers. The following patterns can be identified:

• Almost 50% of the population is part of the White Alone race and nearly 30% of the Asian Alone. The Two or More Races category represents approximately 7% of the total, 6% and 4% for the Black or African American and Some Other Race Alone categories. The American Indian and Alaska Native Alone and Native Hawaiian and Other Pacific Islander Alone categories are almost unrepresented.
• The White Alone and Asian Alone categories proportions have two opposite trends: the White Alone category proportion increases with the education attainment while the Asian Alone race proportion decreases. Compared to the global San Francisco county repartition, the White Alone category is much underrepresented in the educational attainment categories lower than Some college or associate’s degree, while the Asian Alone race is much overrepresented. The proportions of these two categories is the closest one to the total for the Some college or associate’s degree educational attainment. However, the balance is flipped for the Bachelor’s degree or higher level: the White Alone category is now overrepresented and the Asian Alone are underrepresented.
• The Some Other Race Alone and Black or African American categories proportions have a same evolution for an educational attainment higher than Less than high school diploma: an overrepresentation in the High school graduate (includes equivalency) and Some college or associate’s degree levels, but a severe underrepresentation for the Bachelor’s degree or higher educational attainment (especially for the Black or African American population). There is also a much higher proportion of the Some Other Race Alone category among the Less than high school diploma educational attainment compared to its global proportion among the San Francisco county population.
• As for the Two or More Races category, its representation compared to the global San Francisco county is: slightly lower in the Less than high school diploma level, roughly equal for the High school graduate (includes equivalency) and Bachelor’s degree or higher levels, slightly higher for the Some college or associate’s degree level.
• Because of their very low proportions among the total population of the county, it is hard to raise trends for the American Indian and Alaska Native Alone and Native Hawaiian and Other Pacific Islander Alone categories. From the bar chart representation, I assume that the proportion of Native Hawaiian and Other Pacific Islander Alone is quite representative for every educational attainment. I assume the same for the American Indian and Alaska Native Alone, except for the Bachelor’s degree or higher level because this population seems to be unrepresented.

A comparison with the Educational attainment by race in the Bay Area is available here.

Inputs

The stacked bar chart shows that the global education attainment within the Bay Area is much more distributed than in the San Francisco county only.

• The White Alone population is still overrepresented among the Bachelor’s degree or higher category, but seems more proportionally distributed among the lower categories.
• The Asian population seems to be more represented in terms of proportions in the Bachelor’s degree or higher category compared to the situation in the San Francisco county, and less represented in lower categories.
• The Two or More Races population representation decreases when the educational attainment increases. In fact, the race is overrepresented in the low educational attainment, especially the Less than high school diploma category.
• The Native Hawaiian and Other Pacific Islander Alone population is mostly represented in the categories higher than Less than high school diploma.

Once again, the filled bar chart gives information on the proportion of each race in every category instead of the total numbers. The following patterns can be identified:

• The Asian Alone category proportion is smaller among the Bay Area population than it is in the San Francisco county, while the White Alone, Some Other Race Alone and Black or African American categories proportions are slightly higher.
• Compared to the San Francisco county’s educational attainment repartition, the White Alone and Some Other Race Alone categories proportions are much more important in the Less than high school diploma educational attainment, while the Asian Alone category is much less represented and Black or African American slightly less. However, these two last categories proportions in this educational attainment are roughly equivalent to their global proportion among the Bay Area educational system, while the White Alone category proportion is very low and the Some Other Race Alone proportion is very high.
• In the High school graduate educational attainment, the same patterns are noticeable for the White Alone and Asian Alone categories proportions compared to the San Francisco county’s repartition. The Some Other Race Alone category proportion is still slightly disproportionate, and the Black or African American population proportion is also a bit higher than the global one. It is interesting to raise that the Native Hawaiian and Other Pacific Islander Alone category proportion is much higher compared to their global proportion in the Bay Area.
• The Some college or associate’s degree educational attainment shows the closest repartition to the Bay Area’s. The only noticeable differences are the slightly higher proportions of the Two or More Races and Black or African American categories and the much lower proportion of the Asian Alone category. Compared to San Francisco county proportions in the same educational attainment, the White Alone race is still overrepresented and the Asian Alone population underrepresented.
• The Bachelor’s degree or higher shows the closest repartition to the San Francisco county’s in the Bay Area. However, the White Alone race is now a bit underrepresented and the Asian Alone population slightly overrepresented in the Bay compared to San Francisco county only. Compared to the repartition in the Bay, this educational attainment is the closest one. The only observable dissimilarities is the higher proportion of the Asian Alone category and the slightly lower proportions of the Some Other Race Alone and Black or African American categories.

Inputs

For the following comments, I will define the San Francisco county’s geography as follows:

• Middle (Mid) SF: Le Castro, Bernal Heights and Haight-Ashbury
• South-East (S-E) SF: Portola, Hunters Point, Visitacion Valley and Excelsior District
• South-West (S-W) SF: Merced and Westwood areas
• West (W) SF: Sunset District and Forest Hill
• North-West (N-W) SF: Richmond District
• North-East (N-E) SF: Financial District, North Beach and Treasure Island
• East (E) SF: Mission District and Dogpatch

I observe three different patterns: in the W, S-W and Mid SF, all K-12 students have an internet access at home. In the top part of the county (N-W, N-E and E SF), a majority of K-12 students have an internet access at home, but there is nonetheless 269 students not benefiting from it in this area. The S-E SF clearly stands out from the other counties. The map shows a much higher percentage of K-12 students with no internet access at home (2.54%) than the other areas.

I find it interesting to put these numbers in perspective with the total number of K-12 students in each part of the county to have a real perspective on the issue. In my opinion, using the percentage data is not adapted to the San Francisco county because he is much smaller than the other counties in the Bay. Therefore, the surface covered by one of its section is also much smaller than in the others. Analyzing percentages is relevant when there is a huge difference in student populations between the sections of a county due to the concentration of educational institutions, because it takes into account the concentration of student population in a defined area. Here, the areas borders are so closed to each other that a K-12 student might be studying in another section than the one he is living in. A quick argument is that if 61 students represent 0.12% of the N-W SF K-12 student population, then the 618 students with no internet access at home in the S-E SF would have represented only 1.21% of the N-W SF K-12 student population, when it represents more than 2.5% in the S-E area.

It makes me think that this is in fact a problem of neighborhood inequalities that leads to inequalities in education. This is why I chose to plot the same map but using the total number of K-12 students with no internet access at home as scale instead of the percentage to support my idea. I think this map allows to better visualize the differences and inequalities among the different areas of the county. In fact, the top part of the county clearly stands out from the W/S-W/Mid part of the county. The S-E part of SF still appears to have much more K-12 students with no internet access at home than the others.

To conclude, I wanted to highlight through the specific study of the San Francisco county that the inequalities in the access to education (here symbolized by an internet access at home) are directly related to neighborhood inequalities. According to the Bloomberg CityLab, these consist in “the juxtaposition of concentrated disadvantage and concentrated advantage at the extremes”. Here, the S-E SF clearly suffers from these inequalities, and the K-12 students living in this area might be impacted in their ability to pursue higher level studies, or even succeed in the on they are enrolled in.

The study of the Percentage of K-12 students with no internet access at home in the Bay Area is available here.

Inputs

The first map shows clear disparities among the different counties in the Bay Area. The San Francisco county studied before appears to have the lowest percentages of K-12 students with no internet access at home, and the Alameda county (especially South-Eastern Oakland) clearly stands out from the other areas in the Bay. This is why I will isolate the Alameda county for a dedicated part and will analyze the global Bay without this county instead. In fact, the very high percentage (13.93%) does not allow a proper visualization of the contrasts among the other counties/regions.

I removed the Alameda county for the second map, which reveals much more about the inequalities in internet access at home for K-12 students in the Bay. In fact, I can notice three patterns:

• The suburban areas of big cities are much unprivileged compared to the center. San Jose clearly pictures this phenomenon: the center of the city has no K-12 students with no internet access at home, while the suburbs regroup more than 3000 unprivileged K-12 students.
• Poorer cities adjacent to very rich cities also show high percentages. For example, Redwood City and San Carlos have much higher numbers than Palo Alto and Mountain View. Additionally, it could be interesting to have more insight of the local repartition of these students. In fact, North Palo Alto has a high percentage, but I would like to be able to know if the students with no internet access at home live close to the border with Redwood City or not. San Rafael also have very high percentages compared to North San Francisco and Richmond.
• Cities far from the economical center of the Bay are also much unprivileged. The cities of Vacaville, Dixon, Santa Rosa, Morgan Hill et Gilroy clearly highlight this phenomenon.

These patterns raise the issue of a vicious circle when it comes to inequalities among cities: the richer a city gets (in terms of job opportunities and economic activity), the better the offer for education is (because the infrastructures are adapted accordingly), the more expensive the housing becomes (because of the elevation of living standards), and eventually the city becomes even richer.

However, as I mentioned before, the Alameda county clearly shows very high percentages while none of these patterns seem to be applicable here. This is why I will further analyze the Percentage of K-12 students with no internet access at home in the Alameda county here in order to put it in perspective with the results obtained for the San Francisco county.

Inputs

The South-Eastern part of Oakland city stands out on the first map: nearly 14% of the K-12 students here have no internet access at home. The second highest percentage amounts to “only” 2.62% (Dublin, Pleasanton and Livermore area). As for the San Francisco county analysis, I plot a second map showing the total numbers instead of percentages to visualize the inequalities more efficiently at a local scale.

Inequalities are more highlighted on this second map. In fact, there are more K-12 students with no internet access at home in South-Eastern Oakland only, than in all the others area regrouped: 4,960 students in South-Eastern Oakland for 4,873 in the other areas (50.44% of the total only in South-Eastern Oakland). In fact, San Francisco county appears to offer a much more universal access to internet access at home for K-12 students: the total number of K-12 students with no internet access at home in South-Eastern Oakland (4,960) is more than five times higher than the total number in the San Francisco county (887). Overall, the total number of K-12 students with no internet access at home in the Alameda county (9,833) is more than 10 times higher than the total in the San Francisco county.

An interesting data that I cannot plot here is the total number of K-12 students in the two counties, because I compute the total by multiplying the percentages, which can be equal to zero, resulting in a total number of students of zero.

Inputs

I use the following definitions for the variables I will plot and analyze:

• Internal net: Educational mobility from inside the county.
  • A positive Internal net for a certain educational attainment could mean that people within the county have increased their education level (got a degree, graduated…), or a higher number of answers to the ACS between 2019 and the previous years.
  • A negative Internal net should be the consequence of a positive one and the two should balance (except for unusual cases such as deceases).
• External net: Educational mobility coming from outside the county.
  • A positive External net for a certain educational attainment means that people with this level of education have settled in the county.
  • A negative External net for a certain educational attainment means that people with this level of education have left in the county.

First, I assume that the high Internal net for the Less than High school graduate is mainly due to the new enrollments for young students. The Internal net values for the other educational attainment categories add up to a total of +570. As mentioned before, it is quite close to a break-even; the Bachelor degrees and High school graduate (includes equivalency) categories show a negative Internal net leading to a positive one for the Graduate or professional degree and Some college or associate’s degree. The fact that the Internal net is slightly higher than 0 might be due to several factors, such as a higher number of people answering the ACS in 2019 compared to 2018 and the previous years.

The External net is however extremely negative (-12,027 in total), especially among the Bachelor degrees (-6,881, representing more than 50% of the global leavings). Many reasons can lead to such a result. First, the high rent price of housing in the San Francisco county might force the students to live outside the county to pursue their studies there. Also, students willing to pursue higher level studies might suffer from a lack of educational institutions among the county, forcing them to move to an other one. Because the Bachelor degrees category is the one with the extreme negative External net value, I may conclude that it is mainly due to a a lack of educational institutions.

In fact, the San Francisco county is proportionally very small compared to its student population. Therefore, there are two possibilities: there is not enough place for every students willing to pursue higher level studies, or the reputation of the institutions is not good enough to attract the students. This last possibility is even more relevant because of the housing price in the county: high level studies are very expensive and time-consuming, so accumulate the tuition fees with a high rent could be impossible for a standard student.

The study of the Educational mobility in the Alameda county is available here.

Inputs

I use the same definition of Internal and External net as for the San Francisco county.

The situation is very different in the Alameda county. The Less than high school graduate category shows a negative Internal net, which means than the enrollment rate of new students is lower than the rate of students pursuing higher level studies. The high rate of K-12 students with no internet access at home mentioned in the previous part might explain the choices of the families to move out in order to enroll their young children in a place where they can get a better access to every source of education.

Both the High school graduate and Some college or associate’s degree categories’ Internal net are extremely negative (-7,992 and -8,890, which amounts to -16,882 in total), and the Graduate or professional degree and Bachelor’s degree categories’ Internal net are highly positive (+6,148 and +18,271, which amounts to +24,419 in total). Therefore, students in the Alameda county tend to graduate more than in the San Francisco county. The presence of UC Berkeley inside this county might explain why and support the argument of a lack of high level and affordable educational institutions in the San Francisco county.

To compare with the San Francisco county, I notice that the global Internal net for the categories other than Less than high school graduate adds up to +7,537. This is mainly due to the high Internal net for the Bachelor’s degree category. Reasons explaining that could be a high number of students coming in the county to pursue undergraduate studies and starting to fill the ACS as Alameda’s citizens only after they get their bachelor’s degree, or the filling of ACS by non-US citizens students coming to the county to pursue graduate study lasting more than one year or work for the second consecutive year in the county after they completed their undergraduate in a foreign country.

The External net is way more balanced than the Internal net. All categories considered, it amounts to +2,680. However, it does not mean that the county is attractive in terms of educational opportunities. Most of the positive External net is related to the Bachelor’s degree (+6,034) and Graduate or professional degree (+1,515) educational attainment categories. Yet, people with such an educational attainment tend more to settle to work than to pursue higher level studies. On the opposite, the categories more willing to pursue higher studies (Some college or associate’s degree and High school graduate) show negative numbers (respectively -6,028 and -15), which supports my argument.

Also, it is interesting to raise the issue of the Less than high school graduate category: while the Internal net is negative, the External net is positive. It means that the usual inhabitants of the Alameda county with less than a high school diploma as educational attainment tend to leave the county, while there are still newcomers with the same educational attainment. Because I assumed earlier that the departures should mean a low quality of learning access (cf. high percentage of K-12 students with no internet access at home), the arrivals could in fact prove that this poor access to learning tools might not be a generalized phenomenon in the county, but rather a localized issue. The data provided this approach are however not sufficient to give a proper answer to that question.

The study of the Educational mobility in the Solano county is available here.

Inputs

The variables studied and definitions remain the same as for the two previous counties (San Francisco county and Alameda county).

The Solano county shows interesting results, leading to the questioning of the relevance of this method of analysis of the educational mobility. In fact, despite a very low percentage of K-12 students with no internet access at home supposedly testifying an environment conductive to a good access to education, it appears to be a situation pretty similar to the Alameda county:

• The Less than high school graduate, High school graduate and Some college or associate’s degree categories prompts negative Internal net values. While the values for the Less than high school graduate categories are quite similar for the Solano and Alameda counties (-1,301 against -1,305), they are less extreme for High school graduate and Some college or associate’s degree categories (-1,262 against -7,992, and -5,697 against -8,890). This means that the poor access to internet at home in the Alameda county is not the only issue leading to the departures of students: the lack of educational institutions is also very impacting. I have already raised this issue for the San Francisco county, thus the Solano county adds weight to it.
• The Bachelor’s degree and Graduate or professional degree categories prompts positive Internal net values, but much lower (+2,346 against +18,271, and +2,711 against +6,148). Once again, this raise the issue of the lack of educational institution, because the high numbers in the Alameda county could be explained by the presence of huge universities such as UC Berkeley. Also, the Solano county does not suffer from high rent prices like the San Francisco county. Therefore, the economic factor should not weight much here.

When it comes to the External net values, these are highly positive (+5,020 net total) compared to the two other counties (+2,680 in the Alameda county and -12,027 in the San Francisco county).